Method and apparatus for partial interference cancellation in a communication system

ABSTRACT

A method of partial interference cancellation of a received signal including a first data component and a second data component includes the steps of  
     determining a characteristic of the communication channel, estimating a factor based upon the characteristic, using the factor to cancel the second data component from the signal, and recovering the first data component from the signal.

FIELD OF THE INVENTION

[0001] The present invention relates generally to communication systemsincluding wireless communication systems, and more particularly, to amethod and apparatus for providing partial interference cancellation ina wireless communication system.

BACKGROUND OF THE INVENTION

[0002] Wireless communication systems including those based on directsequence spread spectrum (DSSS) code division multiple access (CDMA)technology offer many benefits for cellular radio communications. Inconventional CDMA receivers, known as single-user detectors (SUD), eachuser's data is estimated without consideration of the other users thatare communicating simultaneously. The other users appear as backgroundnoise. These conventional receivers typically utilize simple correlationreceivers that correlate the received signal with a synchronized copy ofthe desired user's spreading signal. An alternate approach is to employa multi-user detector (MUD) that simultaneously demodulates all userswithin a CDMA bandwidth.

[0003] Consideration of the other users in detecting a particular user'ssignal can significantly improve the receiver's performance metrics. Theimprovement in performance of the MUD over the SUD is manifested eitheras a reduction in the required energy per bit (E_(b)) for a specifiedquality of service (QoS) for a fixed number of users, or as an increasein the number of users supported at the specified QoS of the same E_(b).While the former offers the potential benefit of extending the lifetimeof subscriber unit (mobile station) batteries and of reducing theoverall interference in a CDMA cellular system, the latter represents apotential increase in the capacity of the system.

[0004] There are several design approaches for a MUD receiver. Oneapproach is to remove from the received signal the estimatedcontribution of the other users, or what is referred to as themultiple-access interference (MAI). The estimated MAI may be entirelyremoved in a “brute-force” interference cancellation (IC) approach oronly partially removed in so-called partial interference cancellation(PIC). The user's transmitted information is then estimated from the“cleaned” signal. Receivers that incorporate MAI reduction, or IC, areknown as subtractive MUD. The performance of these receivers depends onthe quality of the MAI estimates. The performance of these receiversalso depends on the partial interference coefficients used to estimatethe received signal. If the estimates are poor, the job of suppressingMAI may turn out to be ineffective. It is typical that hard estimatesand fixed brute-force coefficients are used, which in some cases, maycause the MUD to perform worse than a conventional SUD.

[0005] Thus, there is a need for a method and apparatus for partialinterference cancellation in a communication system, and particularly,for method and apparatus for enhancing the quality of the data estimatesand cancellation coefficients utilized in providing partial interferencecancellation.

BRIEF DESCRIPTION OF THE DRAWINGS

[0006]FIG. 1 is a block diagram representation of a communication systemthat may be adapted in accordance with a preferred embodiment of theinvention.

[0007]FIG. 2 is a graph illustrating a piece-wise linear functionemployed in a preferred embodiment of the invention.

[0008]FIG. 3 is a flow chart illustrating a method of providing dataestimates in accordance with a preferred embodiment of the invention.

[0009]FIG. 4 is a graph illustrating a piece-wise linear functionemployed in a preferred embodiment of the invention.

[0010]FIG. 5 is a flow chart illustrating a method of providing partialinterference cancellation coefficients in accordance with a preferredembodiment of the invention.

[0011]FIG. 6 illustrates plots comparing performance of a systemconstructed in accordance with the preferred embodiments of theinvention with prior art systems.

[0012]FIG. 7 illustrates plots comparing performance of a systemconstructed in accordance with the preferred embodiments of theinvention with prior art systems.

[0013]FIG. 8 illustrates plots comparing performance of a systemconstructed in accordance with the preferred embodiments of theinvention with prior art systems.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] In a method according to a preferred embodiment of the invention,despread data is utilized to generate soft estimates of multi-user dataon a power control group (PCG) by power control group basis. The softdata estimates are made based upon a signal-to-noise ratio estimate andan applied functional approximation. The soft data estimates are thenused in a multi-access interference cancellation approach to improve theestimation of the coded information sequence, d, for a particular user.

[0015] In a method according to an alternate preferred embodiment of theinvention, despread data is utilized to determine partial interferencecancellation coefficients that are utilized in a partial interferencecancellation approach to improve the estimation of the coded informationsequence, d, for a particular user.

[0016] In one preferred embodiment of the invention, the appliedfunctional approximation is a piece-wise linear approximation of thehyperbolic tangent function (tanh). In another preferred embodiment ofthe invention, the applied functional approximation is a piece-wiselinear approximation of a probability error function.

[0017] Referring to FIG. 1 of the drawings, a digital communicationnetwork 10 includes a radio access network 12 including a base station14 and a base station controller 16. The radio access network 12 iscoupled to a switch fabric 18, which may be a circuit switch network ora packet data network that interconnects the radio access network 12with a public switched telephone network 22 and other radio or datanetworks 24. The base station 14 provides wireless communicationservices to mobile stations 20 operating within a coverage area of thebase station 14. Preferably the base station 14 operates in accordancewith one or more wireless communication standards, including withoutlimitation a direct sequence code division multiple access (DS-CDMA)system operating in accordance with the IS-95 3G standard.

[0018] For any end-user, i.e., mobile station 20, the i^(th) chip of anIS-95 3G spread digital signal S can be modeled as:

S _(i)=(P _(pi) +jKd _(i) w _(i))c _(i)

[0019] and consists of a pilot component, P_(pi); and a data-bearingcomponent; Dd_(i)w_(i), where P and D are the corresponding amplitudes;p is the pilot sequence; d is the interleaved and possibly-repeatedcoded information sequence; w is the Walsh-code sequence correspondingto the data-bearing component; and c denotes the product of the shortand long pseudo-random noise (PN) sequences.

[0020] The signal S goes through a pulse-shaping filter for transmissionover the air and is received by a receiver, e.g., the signal S istransmitted by mobile station 20 and is received by base station 14. Thedata from each receiver antenna at base station 14 is then matchfiltered and sampled; at the chip rate, the result for a particularfinger is:

r _(i) :=s _(i) h _(i) +ISI _(i) +TN _(i) +MAI _(i)

[0021] where h is the complex-valued channel coefficient; ISI isinter-symbol interference; TN is the receiver thermal noise; and MAI ismulti-access interference.

[0022] The ultimate goal of the receiver is the recovery of the codedinformation sequence, d. In a MUD receiver incorporating IC, the MAI issubtracted from the received signal to form a “cleaned” signal fromwhich d may be recovered. Actually, it is an estimate of the MAI that issubtracted. Estimating MAI, i.e., estimating r, requires estimating bothS and d. Previously “hard” estimates. +1, −1, have been used for d. Inaccordance with a preferred embodiment of the invention, a soft estimateof d is provided.

[0023] To estimate r, h(0) and d(0) denote the despread pilot componentand the despread data component, respectively. An estimate h(1) of Ph isobtained by passing h(0) through a channel estimation filter f, i.e.,hi(1):=(f*H(0))i, where * denotes discrete convolution.

[0024] The soft data estimates d_(i) ⁽¹⁾ are obtained as follows. First,the d_(i) ⁽⁰⁾, generated by dispreading the data component are phasecompensated using the h_(i) ⁽¹⁾,${\hat{d}}_{i}:={\sum\limits_{a = 1}^{A}{\sum\limits_{m = 1}^{M}{d_{a,m,i}^{(0)}\left( h_{a,m,{n{(i)}}}^{(1)} \right)}^{*}}}$

[0025] where A is the number of receiver antennas; M_(a) is the numberof fingers assigned to resolve rays or multi-path components for antennaa; and x* denotes the complex conjugate of x. Second, applying asimplifying assumption that ISI_(i), TN_(i), MAI_(i), and the estimationerrors in h_(i) ⁽¹⁾ are all uncorrelated and Guassian, then

{circumflex over (d)} _(i) =jμd _(i) +w _(i)

[0026] where μ>0 and w_(i) denotes a complex-valued, Gaussian randomvariable whose independent components have mean zero and variance σ².Under this assumption, the conditional expectation of d_(i) given{circumflex over (d)}_(i) is E[d_(i)/{circumflex over(d)}_(i)]=tanh(μIm{{circumflex over (d)}_(i)}/σ²}. Third, μ and σ² areestimated on a PCG-by-PCG basis as:$\sigma^{2}:={{1/\left( {i_{2} - i_{1}} \right)}{\sum\limits_{i = i_{1}}^{i_{2}}\left( {{Re}\left\{ {\hat{d}}_{i} \right\}} \right)^{2}}}$$x = {{1/\left( {i_{2} - i_{1}} \right)}{\sum\limits_{i = i_{1}}^{i_{2}}\left( {{Im}\left\{ {\hat{d}}_{i} \right\}} \right)^{2}}}$

{circumflex over (μ)}:=|x−{circumflex over (σ)} ²|^(½)

[0027] where i₁<=i<=i₂ includes the indices of all coded bits within aspecific PCG.

[0028] Although the tanh function may be used, in a preferredimplementation of the invention, the tanh function is approximated by anapplied function t; hence the soft data estimate of d_(i) is:

d _(i) ⁽¹⁾ :=t({circumflex over (μ)}Im{{circumflex over (d)} _(i)}/σ²)

[0029] A preferred choice of the applied function t is a piece-wiselinear function: for zε[0,2.4], this t is obtained by linearinterpolation using the (z,t(z))-pairs (0, 0), (0.625,0.5721),(1.25,0.8658), and (2.4, 1); for z>2.4, t(z):=1; finally, for z<0, t(z):=−t(−z). The function t is illustrated in FIG. 2.

[0030] As will be appreciated from the foregoing discussion, theestimation {circumflex over (d)}_(i) includes an imaginary component anda real component, where the imaginary component is both signal and noiseand the real part is only noise. The estimate σ² is an estimate of theaverage noise power while the estimate x is an average of the signal andnoise power. Thus, the estimate μ, the difference of x and σ², is thesignal. It will be further appreciated that the estimation {circumflexover (d)}_(i) is obtained at the chip level, and hence, IC isaccomplished at the chip level. A re-spreading operation is performed togenerate the “cleaned” signal for the final estimation of the codedinformation sequence d.

[0031] Referring now to FIG. 3, a method 100 of providing a dataestimate begins at step 102 by estimating a signal-to-noise ratioincluding a first signal term σ² and second signal term μ for a receivedbaseband signal. At step 104, an applied function t is used to determinethe soft data estimate on a PCG-by-PCG basis for each user. At step 106,the soft data estimates of each other user is subtracted from thereceived baseband signal. The result is the SNR for the particular userof interest is improved.

[0032] For partial interference cancellation, the estimate of r(l) ofs_(i)h_(i) may be written as:

r _(i) ⁽¹⁾:=(α_(p) p _(i) ′+jα _(a) ηd _(i) ⁽¹⁾ w _(i))c _(i) h _(n(i))⁽¹⁾

[0033] where α_(p) and α_(d) and ad are the partial cancellationcoefficients p_(i)′=1 over the first ¾ of each PCG and p_(i)′=0otherwise, η:=D/P; and d_(i) ⁽¹⁾ is an estimate of d_(i). Since the databits d₁ have a higher rate than the output samples of the filter f, themapping n(.) is needed to match them appropriately: if the sampling rateof ƒ is ν_(i) Hz and the d_(i) have a rate of ν₂ bits/s, then n(i):=[iν₁/ν₂] (hence, each channel estimate is used for the phasecompensation of ν₂/ν₁ bits).

[0034] In accordance with a further preferred embodiment of theinvention, the partial interference cancellation coefficients α_(p) andα_(d) may also be estimated on a PCG-by-PCG basis. For the purpose ofthis embodiment, a hard estimate of d_(i) ⁽¹⁾ is used and is

{circumflex over (d)} ₁ :=sgn(Im{{circumflex over (d)} ₁})

[0035] by recalling that the imaginary part of the {circumflex over(d)}_(i) represents only signal, taking the sign of {circumflex over(d)}_(i) is typically used as an estimate. The estimation error of thesignal is (r₁−r_(i) ⁽¹⁾), and taking the partial derivative of theestimation error for each of α_(p) and α_(d), respectively, and solvingfor α_(p) and α_(d) provides the following:$\alpha_{p} = \frac{1}{1 + {\rho^{2}/{{{Ph}\left( {iT}_{c} \right.}^{2}}}}$

[0036] and$\alpha_{d} = \frac{{2\beta} - 1}{1 + {\rho^{2}/{{{Ph}\left( {iT}_{c} \right.}^{2}}}}$

[0037] where β:=P[d_(i)=d_(i) ⁽¹⁾], i.e., the probability that the dataestimate is correct and ρ² is the variance of the error in estimatingthe product Ph(.), and wherein T_(c) is the duration of the chip.

[0038] In accordance with the preferred embodiments of the invention, βis determined in real time. Using the simplified statistical model for{circumflex over (d)}_(i) from above, the conditional probabilitydensity function of d_(i) ⁽¹⁾ given d_(i) is Guassian with mean μd_(i)and variance σ². Then, assuming that P[d_(i)=1]=P[d_(i)=−1]=½, itfollows that $\begin{matrix}{{1 - \beta} = \quad {P\left\lbrack {d_{1} \neq d_{i}^{(1)}} \right\rbrack}} \\{= \quad \left( {{1/\sqrt{\pi}}{\int_{- \infty}^{{- µ}/\sqrt{2\sigma}}{e^{- t^{2}}\quad {t}}}} \right.} \\{= \quad {{{erfc}\left( {µ/\sqrt{2\sigma}} \right)}/2}}\end{matrix}$

[0039] where${{erfc}(x)}:=\left( {{2/\sqrt{2\pi}}{\int_{x}^{\infty}{{\exp \left( {- t^{2}} \right)}\quad {{t}.}}}} \right.$

[0040] The unknown parameters μ and σ are estimated on a PCG-by-PCGbasis as set forth above. Then an estimate of β is:

{circumflex over (β)}:=1−t({circumflex over (μ)}/{square root}{squareroot over (2{circumflex over (σ)})}

[0041] where the approximation t(x)≈erfc(x)/2 is introduced forpractical implementation. A simple choice for the function t is apiece-wise linear function, for xε[0,1.8], t is obtained by linearinterpolation using the (x,t(x))-pairs(0,0.5), (0.8,0.1), and (1.8,0);for x>1.8, t(x):=0, as shown in FIG. 4.

[0042] From the above equations for α_(p) and α_(d), the choice of(α_(p), α_(d)) in accordance with the preferred embodiment of theinvention is$\left( {\alpha_{p},\alpha_{d}} \right) = \left( {\frac{1}{1 + \gamma},\frac{{2\hat{\beta}} - 1}{1 + \gamma}} \right)$

[0043] where γ is$\gamma:=\frac{T_{a}{f}^{2}}{N{h_{a,m,{n{(i)}}}^{2}}^{2}}$

[0044] where ∥ƒ∥ denotes the l₂-norm of the channel estimation filter η;T_(a), the received power at antenna α averaged over the PCGcorresponding to {circumflex over (β)} and N, the number of pilot chipsused at a time for dispreading the pilot component to obtain h⁽⁰⁾.

[0045] Referring now to FIG. 5, a method 200 of providing partialinterference coefficients begins at step 202 by estimating asignal-to-noise ratio including a first signal term σ² and second signalterm μ for a received baseband signal. At step 204, an applied functiont is used to determine an intermediate parameter on a PCG-by-PCG basis.At step 206, the intermediate parameter is used to determine a firstpartial interference coefficient and a second partial interferencecoefficient, i.e., α_(p) and α_(d).

[0046] One of skill in the art will appreciate that partial interferencecancellation may employ the data estimates and/or the partialinterference coefficients determined on a PCG-by-PCG basis in accordancewith the preferred embodiments of the invention. In this manner,characteristics of the channel itself, e.g., fading conditions orinterference, are accounted for and optimized in the data estimates andcoefficients. Systems utilizing hard data estimates and/or fixedcoefficients do not account for actual channel conditions. The presentinvention provides optimal values in real time to improve theperformance of a receiver utilizing either interference cancellation(IC) or partial interference cancellation (PIC).

[0047] For example, FIG. 6 and FIG. 7 illustrate, by simulation, therequired E_(b)/N_(t) for a given QoS discussed below, where E_(b) is thereceived energy per bit and N_(t) is the receiver's thermal-noise power.The plots show the performance of one stage of IC when d_(i) isestimated in accordance with the preferred embodiments of the invention,hard data estimates and no IC as indicated by the legend. FIG. 6represents 153.6 kbps, circuit-switched, supplemental service, for a QoSof 15% FER with turbo code, Pedestrian A channel with a mobile speed of3 km/h. FIG. 7 represents 9.6 kbps, circuit switched, fundamentalservice for a QoS of 1.5% FER with convolutional code, a flat,Rayleigh-fading channel with a mobile speed of 30 km/h. In bothexamples, the chip rate was 1.2288 Mcps, the receiver had two antennaswith one finger per antenna, the carrier frequency was 2 GHz, and thepower control had a delay of 1.25 ms (corresponding to one PCG) and anerror rate of 4%. The results show that the benefit of using soft dataestimates in accordance with the preferred embodiments of the invention,rather than hard data estimates, becomes significant at high systemloads.

[0048]FIG. 8 shows, by simulation, the required E_(b)/N_(t) for a QoS of1.5% FER in one stage IC when α_(p) and α_(d) are estimated inaccordance with a preferred embodiment of the invention in comparisonwith no IC according to the legend. The simulation is for 9.6 kbps,circuit-switched, fundamental service with convolutional code, a flat,Rayleigh-fading channel with a mobile speed of 30 km/h. The chip ratewas 1.2288 Mcps, the receiver had two antennas with one finger perantenna, the carrier frequency was 2 GHz, and the power control had adelay of 1.25 ms (corresponding to one PCG) and an error rate of 4%.

[0049] While FIGS. 6-8 demonstrate distinct advantages of the invention,one of skill in the art will appreciate that the invention has numerousadditional advantages. For example, the system incorporating a receiverin accordance with the preferred embodiments of the invention offers thepotential for CDMA capacity increase for and lower transmit power for agiven QoS. Lower transmit powers may be correlated to increased batterylife at the mobile station. The invention has been described in terms ofseveral preferred embodiments, and the invention may be otherwiseembodied without departing from its fair scope set forth in thesubjoined claims.

1. A method of interference cancellation comprising: receiving a signalincluding at least a first data component and a second data component ona communication channel; determining a characteristic of thecommunication channel; estimating an interference factor based upon thecharacteristic; using the interference factor to cancel the second datacomponent from the signal; and recovering the first data component fromthe signal.
 2. The method of claim 1, wherein the interference factorcomprises one of a data estimate and a partial interference coefficient.3. The method of claim 1, wherein the signal comprises a spread spectrumcode division multiple access system signal.
 4. The method of claim 1,wherein the step of estimating comprises applying a function to thecharacteristic.
 5. The method of claim 4, wherein the function comprisesa piece-wise linear estimation of the hyperbolic tangent.
 6. The methodof claim 4, wherein the function comprises a piece-wise linearestimation of a probability error function.
 7. The method of claim 1,wherein the characteristic comprises one of a signal estimation and anoise estimation.
 8. In a receiver including interference cancellation,the receiver adapted to receive a signal including a first datacomponent and a second data component, a method of providing a dataestimate comprising the steps of: estimating a signal-to-noise ratio forsignal; applying a function to the signal-to-noise ratio to determine asoft data estimate on a PCG-by-PCG for each of the first data componentand the second data component; and subtracting from the signal the softdata estimate of the second data component.
 9. The method of claim 8,wherein the step of estimating a signal-to-noise ratio comprisesestimating a first signal term and second signal term.
 10. The method ofclaim 8, wherein the function comprises a piece-wise linear estimationof the hyperbolic tangent.
 11. In a receiver including partialinterference cancellation, the receiver adapted to receiver a signalincluding a first data component and a second data component, a methodof providing a partial interference coefficient comprising the steps of:estimating a first signal term and a second signal term of the signal;applying a function to the signal-to-noise ratio to determine anintermediate parameter on a PCG-by-PCG basis; using the intermediateparameter to determine a partial interference coefficient.
 12. Themethod of claim 11, comprising the step of using the intermediateparameter to determine a second partial interference coefficient. 13.The method of claim 11, wherein the function comprises a piece-wiselinear estimation of a probability error function.